Movelets: A dictionary of movement

Abstract

Recent technological advances provide researchers with a way of gathering real-time information on an individual's movement through the use of wearable devices that record acceleration. In this paper, we propose a method for identifying activity types, like walking, standing, and resting, from acceleration data. Our approach decomposes movements into short components called "movelets", and builds a reference for each activity type. Unknown activities are predicted by matching new movelets to the reference. We apply our method to data collected from a single, three-axis accelerometer and focus on activities of interest in studying physical function in elderly populations. An important technical advantage of our methods is that they allow identification of short activities, such as taking two or three steps and then stopping, as well as low frequency rare(compared with the whole time series) activities, such as sitting on a chair. Based on our results we provide simple and actionable recommendations for the design and implementation of large epidemiological studies that could collect accelerometry data for the purpose of predicting the time series of activities and connecting it to health outcomes.

Fig 1

Two segments of accelerometer data. First, a subject walks for approximately 20 second; then, a subject preforms two replicates of ”Lie down / Rest / Stand up”. Acceleration in three mutually orthogonal directions is shown, and activity labels are included.

Fig 2

A display of matching an unlabeled movelet M_i(t*) to 4 chapters in the dictionary. Points in each chapter represent labeled movelets corresponding to the activity associated with this chapter. The distance between the unlabeled M_i(t*) and each chapter is given by the minimum distance between M_i(t*) and the movelets in each chapter. After M_i(t*) is compared to all reference movelets in the dictionary, it is matched to Chapter 2 which provides the smallest distance among all the 4 chapters.

Fig 3

The chapter “Standing from Lying”, which consists of 16 movelets. In dark grey is the section of the acceleration data used to construct the chapter; in light grey are time points with the same activity label, but that are excluded from the chapter as “lazy” movelets.

Fig 4

Observer-defined annotations and predictions for two segments of accelerometer data with several activity types. Curves giving the smallest distance between movelets and each chapter are displayed.

Fig 5

Comparison of “combined observer” annotations, based on observed-defined annotations and an inspection of the raw accelerometer data, and predicted labels.